class_docstr: Class for Tree search based optimisation. 
docstr: Split children. 
docstr_tok: ["Split","children","."]
code: def split_children(self, current, rho, nu, option=1):
      """Split children."""
      pcell = list(current.cell)
      span = [abs(pcell[i][1] - pcell[i][0]) for i in range(len(pcell))]
      if self.Randomize:
          dimension = np.random.choice(range(len(pcell)))
      else:
          dimension = np.argmax(span)
      dd = len(pcell)
      if dimension == current.dimension:
          dimension = (current.dimension - 1) % dd
      cost = 0
      h = current.height + 1
      l = np.linspace(
          pcell[dimension][0], pcell[dimension][1], self.K + 1
      )
      children = []
      for i in range(len(l) - 1):
          cell = []
          for j, _ in enumerate(pcell):
              if j != dimension:
                  cell = cell + [pcell[j]]
              else:
                  cell = cell + [(l[i], l[i + 1])]
          child, c = self.querie(
              tuple(cell), h, rho, nu, dimension, option
          )
          children = children + [child]
          cost = cost + c
  
      return children, cost
  
imports: from __future__ import division
  from functools import total_ordering
  from argparse import Namespace
  import numpy
  from .general_utils import map_to_bounds
label_desc: Use image node_id 5 for calling the OptTree obj's underlying member method code with example usage: obj.split_children(current, rho, nu, option) and returns: children,  cost